Security Vulnerabilities Identified in Azure Machine Learning Service
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🧩 Security Vulnerabilities in Azure Machine Learning Service Exposed. Recent research has highlighted significant security issues within Azure Machine Learning (AML) service, particularly concerning excessive permissions on Storage Accounts that can lead to code execution in Jupyter notebooks. Attackers with read/write access can modify notebooks, potentially executing malicious code without user interaction. A previously identified vulnerability allowed privilege escalation from the Reader role, enabling unauthorized access to execute code. Additionally, a tool was developed to automate the extraction of stored credentials from AML workspaces. These findings underscore the need for enhanced security measures and monitoring to protect against potential exploitation in cloud-based machine learning environments.
